Binary Tree Block Encoding of Classical Matrix
State preparation and block-encoding are essential subroutines in quantum computing. The former provides basic encoding of quantum states, while the latter transforms classical data into a matrix representation within a quantum circuit. Some quantum advantages are built on the assumption that the subroutine– block encoding has been compiled in the quantum circuit, and this derives a problem of how to efficiently compile a block encoding. The resource trade-offs of block encoding, such as circuit size, subnormalization factor, compilation complexity (both time and space), and robustness against errors are central to its efficiency. In this work, the Binary Tree Block-Encoding (BITBLE) protocol is introduced, which optimizes these trade-offs. For a classical matrix in C<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n× 2n</sup>, our approach reduces the compilation time to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i>(n2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n</sup>) using n ancilla qubits, achieving superior resource trade-offs compared to existing methods. Numerical experiments further reveal that the approach outlined in BITBLE enhances compilation efficiency, resource scalability, and robustness against single-qubit gate errors in various standard data encoding tasks. Moreover, all algorithms are available as open-source.
Authors
- Zexian Li (ORCID: https://orcid.org/0000-0002-1433-8075)
- Xiaoming Zhang (ORCID: https://orcid.org/0000-0003-0404-7765)
- Guofeng Zhang (ORCID: https://orcid.org/0000-0001-8793-7338)
- Chunlin Yang (ORCID: https://orcid.org/0000-0001-9350-343X)
Institutions
- Hong Kong Polytechnic University (HK)
- Harbin Engineering University (CN)
- South China Normal University (CN)
- Heilongjiang University (CN)
Publication Details
- Journal
- IEEE Transactions on Quantum Engineering
- Published
- 2025-10-23
- DOI
- https://doi.org/10.1109/tqe.2025.3624699
- Citations
- 1
- Primary Topic
- Matrix Theory and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 1.50